Asia pacific journal of information systems

The Korea Society of Management Information Systems (한국경영정보학회)
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Leveraging Analytics for Talent Acquisition: Case of IT Sector in India

  • Avik Ghosh (Analytics and Information Systems, Xavier Institute of Management) ;
  • Bhaskar Basu (Information Systems, Xavier Institute of Management)
  • Received : 2020.07.22
  • Accepted : 2020.12.10
  • Published : 2020.12.31
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Abstract

One of the challenges faced by Talent Acquisition teams today pertains to the acquisition of human resources by matching job descriptions and skillsets desired. It is more so in the case of competitive sectors like the Indian IT sector. There can be various channels for Talent Acquisition and accordingly, the cost and benefits might vary. However, the consequences of a mismatch have an impact on the quality of deliverables, high recruitment expenses and loss of revenue for the organization. With increased and diverse sources of data that are available to organizations today, there is ample opportunity to apply analytics for informed decision making in this field. This paper reveals useful insights that help streamline the Talent Acquisition process in the Indian IT Industry. The paper adopts a data-centric approach to examine the critical determinants for efficient and effective Talent Acquisition process in IT organizations. Selected supervised machine learning algorithms are applied for the analysis of the dataset. The study is likely to help organizations in reassessing their talent acquisition strategy with respect to key parameters like expected cost to company (CTC), candidate sourcing channels and optimal joining period.

Keywords

Acknowledgement

The authors would like to express their sincere gratitude to Professor U Dinesh Kumar, Chair, Data Centre and Analytics Lab and Indian Institute of Management Bangalore (IIMB) for providing permission and access to the dataset used for this study.

References

  1. Ancker Jr, C. J., and Gafarian, A. V. (1963). Some queuing problems with balking and reneging. I. Operations Research, 11(1), 88-100.
  2. Anschober, M., Bailom, F., Matzler, K., and Richardson, S. (2010). Sustaining corporate success: What drives the top performers? Journal of Business Strategy, 31(5), 4-13.
  3. Ang, S., Slaughter, S., and Yee Ng, K. (2002). Human capital and institutional determinants of information technology compensation: Modeling multilevel and cross-level interactions. Management Science, 48(11), 1427-1445.
  4. Arellano, C., DiLeonardo, A., and Felix, I. (2017). Using people analytics to drive business performance: A case study. McKinsey Quarterly, 6.
  5. Arin, K. P., Huang, V., Minniti, M., Nandialath, A. M., and Reich, O. F. (2015). Revisiting the determinants of entrepreneurship: A Bayesian approach. Journal of Management, 41(2), 607-631.
  6. Bandari, N. K., and Migiro, S. (2015). Talent management in Indian IT and ITES sectors. Indian Journal of Industrial Relations, 51(1), 43-56.
  7. Bersin (2011). How-to-implement-Talent management solutions. NGA.
  8. Solutions Blogs.
  9. Bongard, A. (2019). Automating talent acquisition: Smart recruitment, predictive hiring algorithms, and the data-driven nature of artificial intelligence. Psychosociological Issues in Human Resource Management, 7(1), 36-41.
  10. Boudreau, J. W. (2017). HR must make people analytics more user friendly. Harvard Business Review.
  11. Brahmana, R. K., and Brahmana, R. (2013). What factors drive job seekers attitude in using e-recruitment? The South East Asian Journal of Management, 123-134. doi: 10.21002/seam.v7i2.2050
  12. Buhlmann, P. (2012). Bagging, boosting and ensemble methods. In Handbook of computational statistics (pp. 985-1022). Springer, Berlin, Heidelberg.
  13. Cappelli, P. (2009). Talent on demand-Managing talent in an age of uncertainty. Strategic Direction, 25(3).
  14. Chandra, A., Fealey, T., and Rau, P. (2006). National barriers to global competitiveness: The case of the IT industry in India. Competitiveness Review: An International Business Journal, 16(1), 12-19.
  15. Chandler, J. (2010). The role of location in the recruitment and retention of teachers in international schools. Journal of Research in International Education, 9(3), 214-226.
  16. Cherian, K., and Kamalanabhan, T. J. (2019). Organizational and talent attributes of the Indian IT industry. Employee Relations: The International Journal, 41(5), 876-897.
  17. Chaturvedi, V. (2016). Talent analytics as an indispensable tool and an emerging facet of HR for organization building. FIIB Business Review, 5(3), 13-20.
  18. Choudhury, A., and Medhi, P. (2011). A simple analysis of customer impatience in multi-server queues. International Journal of Applied Management Science, 3(3), 294-315.
  19. Chawla, N. V., Bowyer, K. W., Hall, L. O., and Kegelmeyer, W. P. (2002). SMOTE: Synthetic minority over-sampling technique. Journal of Artificial Intelligence Research, 16, 321-357.
  20. Christersson, M., and Rothe, P. (2012). Impacts of organizational relocation: a conceptual framework. Journal of Corporate Real Estate, 14(4), 226-243.
  21. Dai, G., De Meuse, K. P., and Gaeddert, D. (2011). Onboarding externally hired executives: Avoiding derailment-accelerating contribution. Journal of Management and Organization, 17(2), 165-178.
  22. Duvivier, C., Polese, M., and Apparicio, P. (2018). The location of information technology-led new economy jobs in cities: office parks or cool neighbourhoods? Regional Studies, 52(6), 756-767.
  23. El Ouirdi, M., El Ouirdi, A., Segers, J., and Pais, I. (2016). Technology adoption in employee recruitment: The case of social media in Central and Eastern Europe. Computers in Human Behavior, 57, 240-249.
  24. Robin, E. (2012, Feb 7). Recruitment is not talent acquisition. http://www.bersin.com/blog/post/2012/02/Recruitment-is-NOT-Talent-Acquisition.aspx (Accessed on Feb 7 2012).
  25. Faliagka, E., Tsakalidis, A., and Tzimas, G. (2012). An integrated e-recruitment system for automated personality mining and applicant ranking. Internet Research.
  26. Fitz-enz, J., and Mattox, J. (2014). Predictive analytics for human resources. Wiley, NJ USA, p. 172.
  27. Ghatasheh, N., Faris, H., AlTaharwa, I., Harb, Y., and Harb, A. (2020). Business analytics in telemarketing: Cost-sensitive analysis of bank campaigns using artificial neural networks. Applied Sciences, 10(7), 2581.
  28. Gupta, P. D., Bhattacharya, S., Sheorey, P., and Coelho, P. (2018). Relationship between onboarding experience and turnover intention: Intervening role of locus of control and self-efficacy. Industrial and Commercial Training.
  29. Hassan-Onik, M. M., Miraz, M. H., and Kim, C. S. (2018). A recruitment and human resource management technique using blockchain technology for industry 4.0. In IET Conference Publications, 2018(CP747), 11-16.
  30. Holm, A. B. (2012). E-recruitment: Towards an ubiquitous recruitment process and candidate relationship management. German Journal of Human Resource Management, 26(3), 241-259.
  31. Government of India(GoI) (2020). Invest India, the department for promotion of industry and internal trade, ministry of commerce and industry. Government of India, Available at www.investindia. gov.in/sector/it-bpm (Accessed on June 20 2020).
  32. John, G. H., and Langley, P. (2013). Estimating continuous distributions in Bayesian classifiers. arXiv preprint arXiv:1302.4964.
  33. Jonsson, L., and Thorgren, S. (2017). Trainee programs: An emerging model on psychological contract reciprocity. Personnel Review, 46(8), 1738-1754.
  34. Joseph, D., Boh, W. F., Ang, S., and Slaughter, S. A. (2012). The career paths less (or more) traveled: A sequence analysis of IT career histories, mobility patterns, and career success. MIS Quarterly, 36(2), 427-452.
  35. Rocca, J., and Rocca, B. (2019). Ensemble methods: Bagging, boosting and stacking. Available at https://towardsdatascience.com/ensemble-methods-bagging-boosting-and-stacking-c9214a10a205 (Accessed on November 10 2019).
  36. Korsakiene, R., Stankeviciene, A., Simelyte, A., and Talackiene, M. (2015). Factors driving turnover and retention of information technology professionals. Journal of Business Economics and Management, 16(1), 1-17.
  37. Dinesh, K. (2017). Business analytics: The science of data-driven decision making (2017 ed.). Wiley Publication.
  38. Kumar, S. R. (2013). A study on talent acquisition in sierra Atlantic. Advances in Management, 6(9), 27-31.
  39. Kumar, R., and Dinesh Kumar, U. (2016). HR analytics scaleneworks: Behavioral modeling to predict renege. IIMB(Product#-IMB 551), HBP.
  40. Leon, A. (2016). HR analytics: Talent acquisition. Cornell University, ILR School.
  41. Lockwood, D., and Ansari, A. (1999). Recruiting and retaining scarce information technology talent: A focus group study. Industrial Management & Data Systems, 99(6), 251-256.
  42. Marsden, P. V. (1994). The hiring process: Recruitment methods. American Behavioral Scientist, 37(7), 979-991.
  43. Mayo, A. (2018). Applying HR analytics to talent management. Strategic HR Review, 17(5), 247-254.
  44. Melanthiou, Y., Pavlou, F., and Constantinou, E. (2015). The use of social network sites as an e-recruitment tool. Journal of Transnational Management, 20(1), 31-49.
  45. Morel, D., Kalvin, C. Y., Liu-Ferrara, A., Caceres-Suriel, A. J., Kurtz, S. G., and Tabak, Y. P. (2020). Predicting hospital readmission in patients with mental or substance use disorders: A machine learning approach. International Journal of Medical Informatics, 139, 104136.
  46. NASSCOM (2019). Industry performance: 2018-19 and what lies ahead.
  47. Nandialath, A. M., David, E., Das, D., and Mohan, R. (2018). Modeling the determinants of turnover intentions: A Bayesian approach. Evidence-based HRM: A Global Forum for Empirical Scholarship, 6(1), 2-24.
  48. Noack, B. (2019). Big data analytics in human resource management: Automated decision-making processes, predictive hiring algorithms, and cutting-edge workplace surveillance technologies. Psychosociological Issues in Human Resource Management, 7(2), 37-42.
  49. Oehler, K., and Falletta, S. (2015). Should companies have free rein to use predictive analytics? HR Magazine, 60(5), 26-27.
  50. Oldham, G. R., and Hackman, J. R. (2010). Not what it was and not what it will be: The future of job design research. Journal of Organizational Behavior, 31(2-3), 463-479.
  51. Opitz, D., and Maclin, R. (1999). Popular ensemble methods: An empirical study. Journal of Artificial Intelligence Research, 11, 169-198.
  52. Palshikar, G. K. et al. (2019). Analytics-led talent acquisition for improving efficiency and effectiveness. In A. Laha (ed.), Advances in analytics and applications. Springer Proceedings in Business and Economics, Springer, Singapore. doi: 10.1007/978-981-13-1208-3_13
  53. Panko, R. R. (2008). IT employment prospects: Beyond the dotcom bubble. European Journal of Information Systems, 17(3), 182-197.
  54. Phillips, P. P., and Phillips, J. J. (2019). The state of human capital analytics in developing countries: A focus on the Middle East. Strategic HR Review, 18(5), 190-198.
  55. Pillai, R., and Sivathanu, B. (2020). Adoption of artificial intelligence (AI) for talent acquisition in IT/ITeS organizations. Benchmarking: An International Journal, 27(9), 2599-2629.
  56. Riemsdijk, M. (2013). Talent acquisition in the IT industry in Bangalore: a multi-level study. Tijdschrift voor Economische en Sociale Geografie, 104(4), 478-490.
  57. Ruggs, E. N., Walker, S. S., Blanchard, A., and Gur, S. (2016). Online exclusion: Biases that may arise when using social media in talent acquisition. In Social media in employee selection and recruitment (pp. 289-305). Springer, Cham.
  58. Sahay, P. (2014). Design thinking in talent acquisition: A practitioner's perspective. Strategic HR Review, 13(4-5), 170-180.
  59. SEIR (2007). Software engineering information repository. Software Engineering Institute, Carnegie Mellon University, Pittsburgh, PA.
  60. Sesil, J. C. (2017). Applying advanced analytics to HR management decisions: methods for selection, developing incentives, and improving collaboration. Pearson FT Press, p. 81.
  61. Shmueli, G., and Koppius, O. R. (2011). Predictive analytics in information systems research. MIS Quarterly, 35(3), 553-572.
  62. Simmons, J. P., Nelson, L. D., and Simonsohn, U. (2011). False-positive psychology: Undisclosed flexibility in data collection and analysis allows presenting anything as significant. Psychological Science, 22(11), 1359-1366.
  63. Sonnenberg, M., Van Zijderveld, V., and Brinks, M. (2014). The role of talent-perception incongruence in effective talent management. Journal of World Business, 49(2), 272-280.
  64. Srivastava, P., and Bhatnagar, J. (2010). Employer brand for talent acquisition: An exploration towards its measurement. Vision, 14(1-2), 25-34.
  65. Tafti, M. M., Mahmoudsalehi, M., and Amiri, M. (2017). Critical success factors, challenges and obstacles in talent management. Industrial and Commercial Training, 49(1), 15-21.
  66. Tama, B. A., and Comuzzi, M. (2019). An empirical comparison of classification techniques for next event prediction using business process event logs. Expert Systems with Applications, 129, 233-245.
  67. Thomas, A., Buboltz, W. C., and Winkelspecht, C. S. (2004). Job characteristics and personality as predictors of job satisfaction. Organizational Analysis, 12(2), 205-219.
  68. Tigali, M., and Dasgupta, A. (2014). U.S. patent No. 8,676,717. Washington, DC: U.S. Patent and Trademark Office.
  69. Tursunbayeva, A., Di Lauro, S., and Pagliari, C. (2018). People analytics-a scoping review of conceptual boundaries and value propositions. International Journal of Information Management, 43, 224-247.
  70. Tymon Jr, W. G., Stumpf, S. A., and Smith, R. R. (2011). Manager support predicts turnover of professionals in India. Career Development International, 16(3), 293-312.
  71. Ujlayan, A., and Sharma, M. (2019). A study to enhance candidate screening process using similarity analysis. International Journal of Business and Data Analytics, 1(1). doi: 10.1504/IJBDA.2019. 098832
  72. Vargas, R., Yurova, Y. V., Ruppel, C. P., Tworoger, L. C., and Greenwood, R. (2018). Individual adoption of HR analytics: A fine-grained view of the early stages leading to adoption. The International Journal of Human Resource Management, 29(22), 3046-3067.
  73. Venkatesh, V., Windeler, J. B., Bartol, K. M., and Williamson, I. O. (2017). Person-organization and person-job fit perceptions of new IT employees: Work outcomes and gender differences. MIS Quarterly, 41(2), 525-558.
  74. Walford-Wright, G., and Scott-Jackson, W. (2018). Talent rising: People analytics and technology driving talent acquisition strategy. Strategic HR Review, 17(5), 226-233.
  75. Wu, W., and Issa, R. R. (2013). Impacts of BIM on talent acquisition in the construction industry. In Proc., 29th Annual ARCOM Conference, 35-45.